/// <summary>
/// The OnGetStatus callback fires on each iteration within the Train method.
/// </summary>
public void OnUpdateStatus(GetStatusArgs e)
{
if (m_icallback != null)
{
m_dfLoss = e.Loss;
m_nIteration = e.Frames;
m_nIterations = e.MaxFrames;
m_dfAccuracy = e.Reward;
Dictionary <string, double> rgValues = new Dictionary <string, double>();
rgValues.Add("GlobalIteration", e.Frames);
rgValues.Add("GlobalLoss", e.Loss);
rgValues.Add("LearningRate", e.LearningRate);
rgValues.Add("GlobalAccuracy", e.Reward);
m_icallback.Update(TrainingCategory, rgValues);
}
}
/// <summary>
/// The OnGetStatus callback fires on each iteration within the Train method.
/// </summary>
public void OnUpdateStatus(GetStatusArgs e)
{
m_nIteration = e.Iteration;
m_dfAccuracy = e.TotalReward;
m_nIterations = e.MaxFrames;
m_dfImmediateRewards = e.Reward;
m_dfGlobalRewards = e.TotalReward;
m_dfGlobalRewardsMax = Math.Max(m_dfGlobalRewardsMax, e.TotalReward);
m_dfGlobalRewardsAve = (1.0 / (double)m_nThreads) * e.TotalReward + ((m_nThreads - 1) / (double)m_nThreads) * m_dfGlobalRewardsAve;
m_dfExplorationRate = e.ExplorationRate;
m_dfOptimalSelectionRate = e.OptimalSelectionCoefficient;
if (m_nThreads > 1)
{
m_nGlobalEpisodeCount++;
}
else
{
m_nGlobalEpisodeCount = e.Frames;
}
m_nGlobalEpisodeMax = e.MaxFrames;
m_dfLoss = e.Loss;
if (m_icallback != null)
{
Dictionary <string, double> rgValues = new Dictionary <string, double>();
rgValues.Add("GlobalIteration", GlobalEpisodeCount);
rgValues.Add("GlobalLoss", GlobalLoss);
rgValues.Add("LearningRate", e.LearningRate);
rgValues.Add("GlobalAccuracy", GlobalRewards);
rgValues.Add("Threads", m_nThreads);
m_icallback.Update(TrainingCategory, rgValues);
}
e.NewFrameCount = m_nGlobalEpisodeCount;
if (e.Index == 0 && m_nSnapshot > 0 && m_nGlobalEpisodeCount > 0 && (m_nGlobalEpisodeCount % m_nSnapshot) == 0)
{
m_bSnapshot = true;
}
}